Overview

Dataset statistics

Number of variables14
Number of observations2918
Missing cells0
Missing cells (%)0.0%
Duplicate rows267
Duplicate rows (%)9.2%
Total size in memory319.3 KiB
Average record size in memory112.0 B

Variable types

NUM10
CAT3
BOOL1

Warnings

Dataset has 267 (9.2%) duplicate rows Duplicates
Einzug is highly correlated with AnzGesperrtFsHigh correlation
AnzGesperrtFs is highly correlated with EinzugHigh correlation
Duration is highly skewed (γ1 = 41.99159887) Skewed
TempDist has 2436 (83.5%) zeros Zeros
SpatDist has 2529 (86.7%) zeros Zeros
Length has 819 (28.1%) zeros Zeros
Duration has 469 (16.1%) zeros Zeros

Reproduction

Analysis started2020-10-20 15:51:55.611789
Analysis finished2020-10-20 15:52:10.463382
Duration14.85 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

TempExMax
Real number (ℝ≥0)

Distinct207
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.283756
Minimum9
Maximum1326
Zeros0
Zeros (%)0.0%
Memory size22.8 KiB
2020-10-20T17:52:10.528692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile15
Q142
median111
Q3231
95-th percentile654
Maximum1326
Range1317
Interquartile range (IQR)189

Descriptive statistics

Standard deviation224.7888134
Coefficient of variation (CV)1.187575829
Kurtosis6.945236247
Mean189.283756
Median Absolute Deviation (MAD)78
Skewness2.405027055
Sum552330
Variance50530.01064
MonotocityNot monotonic
2020-10-20T17:52:10.666445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
151725.9%
 
36873.0%
 
21752.6%
 
189732.5%
 
24702.4%
 
60602.1%
 
18541.9%
 
48541.9%
 
30511.7%
 
27471.6%
 
Other values (197)217574.5%
 
ValueCountFrequency (%) 
9381.3%
 
12270.9%
 
151725.9%
 
18541.9%
 
21752.6%
 
ValueCountFrequency (%) 
132660.2%
 
1323190.7%
 
13201< 0.1%
 
11941< 0.1%
 
1116120.4%
 

SpatExMax
Real number (ℝ≥0)

Distinct1178
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11529.66278
Minimum699
Maximum220501
Zeros0
Zeros (%)0.0%
Memory size22.8 KiB
2020-10-20T17:52:10.803793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum699
5-th percentile1453
Q12831
median5472.5
Q312499
95-th percentile33270.55
Maximum220501
Range219802
Interquartile range (IQR)9668

Descriptive statistics

Standard deviation23039.11053
Coefficient of variation (CV)1.998246694
Kurtosis53.56709803
Mean11529.66278
Median Absolute Deviation (MAD)3246.5
Skewness6.824105169
Sum33643556
Variance530800614.2
MonotocityNot monotonic
2020-10-20T17:52:10.932669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
28311394.8%
 
1926592.0%
 
1014391.3%
 
2908281.0%
 
8459240.8%
 
3306200.7%
 
2226190.7%
 
189730170.6%
 
1903150.5%
 
11282140.5%
 
Other values (1168)254487.2%
 
ValueCountFrequency (%) 
6991< 0.1%
 
90220.1%
 
9511< 0.1%
 
96520.1%
 
99120.1%
 
ValueCountFrequency (%) 
22050170.2%
 
21908280.3%
 
189730170.6%
 
12590950.2%
 
660111< 0.1%
 

TempDist
Real number (ℝ≥0)

ZEROS

Distinct30
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.809801234
Minimum0
Maximum29
Zeros2436
Zeros (%)83.5%
Memory size22.8 KiB
2020-10-20T17:52:11.161566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.109545259
Coefficient of variation (CV)2.82326322
Kurtosis10.49844062
Mean1.809801234
Median Absolute Deviation (MAD)0
Skewness3.26262935
Sum5281
Variance26.10745275
MonotocityNot monotonic
2020-10-20T17:52:11.270596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
0243683.5%
 
7371.3%
 
3361.2%
 
6351.2%
 
9331.1%
 
8301.0%
 
2291.0%
 
5230.8%
 
1220.8%
 
4200.7%
 
Other values (20)2177.4%
 
ValueCountFrequency (%) 
0243683.5%
 
1220.8%
 
2291.0%
 
3361.2%
 
4200.7%
 
ValueCountFrequency (%) 
2930.1%
 
2890.3%
 
2790.3%
 
2660.2%
 
25100.3%
 

SpatDist
Real number (ℝ≥0)

ZEROS

Distinct244
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.23372173
Minimum0
Maximum993
Zeros2529
Zeros (%)86.7%
Memory size22.8 KiB
2020-10-20T17:52:11.417582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile473
Maximum993
Range993
Interquartile range (IQR)0

Descriptive statistics

Standard deviation164.4138263
Coefficient of variation (CV)3.272977208
Kurtosis13.36002561
Mean50.23372173
Median Absolute Deviation (MAD)0
Skewness3.677628096
Sum146582
Variance27031.90627
MonotocityNot monotonic
2020-10-20T17:52:11.570042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0252986.7%
 
576421.4%
 
92381.3%
 
43160.2%
 
150.2%
 
22040.1%
 
12040.1%
 
58830.1%
 
6830.1%
 
18230.1%
 
Other values (234)2819.6%
 
ValueCountFrequency (%) 
0252986.7%
 
150.2%
 
230.1%
 
320.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
9931< 0.1%
 
9921< 0.1%
 
9911< 0.1%
 
9861< 0.1%
 
9841< 0.1%
 

Coverage
Real number (ℝ≥0)

Distinct95
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.86326251
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Memory size22.8 KiB
2020-10-20T17:52:11.721912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q127
median45
Q365
95-th percentile84
Maximum100
Range98
Interquartile range (IQR)38

Descriptive statistics

Standard deviation24.01912192
Coefficient of variation (CV)0.5125362733
Kurtosis-0.8816568474
Mean46.86326251
Median Absolute Deviation (MAD)19
Skewness0.2876203236
Sum136747
Variance576.9182179
MonotocityNot monotonic
2020-10-20T17:52:11.863550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
831455.0%
 
84752.6%
 
20662.3%
 
45642.2%
 
100602.1%
 
28592.0%
 
18572.0%
 
25561.9%
 
24521.8%
 
34521.8%
 
Other values (85)223276.5%
 
ValueCountFrequency (%) 
270.2%
 
4110.4%
 
5240.8%
 
630.1%
 
790.3%
 
ValueCountFrequency (%) 
100602.1%
 
9930.1%
 
9740.1%
 
9570.2%
 
9470.2%
 

TimeLossCar
Real number (ℝ≥0)

Distinct767
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1489.782728
Minimum1000
Maximum1999
Zeros0
Zeros (%)0.0%
Memory size22.8 KiB
2020-10-20T17:52:11.993687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1043
Q11229.25
median1467
Q31748.75
95-th percentile1952
Maximum1999
Range999
Interquartile range (IQR)519.5

Descriptive statistics

Standard deviation292.5070139
Coefficient of variation (CV)0.1963420628
Kurtosis-1.195189501
Mean1489.782728
Median Absolute Deviation (MAD)257.5
Skewness0.04494495369
Sum4347186
Variance85560.35319
MonotocityNot monotonic
2020-10-20T17:52:12.133832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
14671394.8%
 
1105602.1%
 
1127471.6%
 
1189240.8%
 
1043230.8%
 
1673220.8%
 
1022220.8%
 
1593190.7%
 
1778190.7%
 
1468190.7%
 
Other values (757)252486.5%
 
ValueCountFrequency (%) 
100040.1%
 
100140.1%
 
10021< 0.1%
 
10031< 0.1%
 
10041< 0.1%
 
ValueCountFrequency (%) 
199940.1%
 
199820.1%
 
1997100.3%
 
199650.2%
 
199530.1%
 

TimeLossHGV
Real number (ℝ≥0)

Distinct472
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean739.3618917
Minimum500
Maximum999
Zeros0
Zeros (%)0.0%
Memory size22.8 KiB
2020-10-20T17:52:12.271670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile524
Q1622
median739
Q3863
95-th percentile968
Maximum999
Range499
Interquartile range (IQR)241

Descriptive statistics

Standard deviation141.4989105
Coefficient of variation (CV)0.1913797723
Kurtosis-1.17594884
Mean739.3618917
Median Absolute Deviation (MAD)121
Skewness0.09722089056
Sum2157458
Variance20021.94167
MonotocityNot monotonic
2020-10-20T17:52:12.421566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7391414.8%
 
630672.3%
 
913431.5%
 
798301.0%
 
919301.0%
 
566270.9%
 
678230.8%
 
691210.7%
 
680200.7%
 
958200.7%
 
Other values (462)249685.5%
 
ValueCountFrequency (%) 
50040.1%
 
502120.4%
 
50320.1%
 
50430.1%
 
50540.1%
 
ValueCountFrequency (%) 
9991< 0.1%
 
99720.1%
 
99640.1%
 
99540.1%
 
99420.1%
 

Strasse
Categorical

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
A 3
992 
A 9
598 
A 7
270 
A 99
246 
A 96
189 
Other values (12)
623 
ValueCountFrequency (%) 
A 399234.0%
 
A 959820.5%
 
A 72709.3%
 
A 992468.4%
 
A 961896.5%
 
A 61866.4%
 
A 931444.9%
 
A 73802.7%
 
A 92792.7%
 
A 70511.7%
 
Other values (7)832.8%
 
2020-10-20T17:52:12.565433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T17:52:12.695464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length3
Mean length3.305003427
Min length3

AnzGesperrtFs
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
1
2003 
0
902 
2
 
11
3
 
2
ValueCountFrequency (%) 
1200368.6%
 
090230.9%
 
2110.4%
 
320.1%
 
2020-10-20T17:52:12.819098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T17:52:12.907750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:12.995145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Einzug
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.541466758
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size22.8 KiB
2020-10-20T17:52:13.096705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.646354834
Coefficient of variation (CV)0.6477971149
Kurtosis-1.263833105
Mean2.541466758
Median Absolute Deviation (MAD)1
Skewness0.6838487736
Sum7416
Variance2.710484238
MonotocityNot monotonic
2020-10-20T17:52:13.283250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
2103135.3%
 
1101334.7%
 
585929.4%
 
3140.5%
 
41< 0.1%
 
ValueCountFrequency (%) 
1101334.7%
 
2103135.3%
 
3140.5%
 
41< 0.1%
 
585929.4%
 
ValueCountFrequency (%) 
585929.4%
 
41< 0.1%
 
3140.5%
 
2103135.3%
 
1101334.7%
 

Richtung
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
1
2857 
0
 
61
ValueCountFrequency (%) 
1285797.9%
 
0612.1%
 
2020-10-20T17:52:13.361073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length
Real number (ℝ≥0)

ZEROS

Distinct1305
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean905.4996573
Minimum0
Maximum21032
Zeros819
Zeros (%)28.1%
Memory size22.8 KiB
2020-10-20T17:52:13.444403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median301.5
Q31168.25
95-th percentile3708.55
Maximum21032
Range21032
Interquartile range (IQR)1168.25

Descriptive statistics

Standard deviation1558.453637
Coefficient of variation (CV)1.721097986
Kurtosis27.96734001
Mean905.4996573
Median Absolute Deviation (MAD)301.5
Skewness4.126283161
Sum2642248
Variance2428777.738
MonotocityNot monotonic
2020-10-20T17:52:13.580644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
081928.1%
 
100170.6%
 
150120.4%
 
200120.4%
 
5290.3%
 
40090.3%
 
6580.3%
 
50080.3%
 
30080.3%
 
9770.2%
 
Other values (1295)200968.8%
 
ValueCountFrequency (%) 
081928.1%
 
81< 0.1%
 
101< 0.1%
 
2620.1%
 
271< 0.1%
 
ValueCountFrequency (%) 
210321< 0.1%
 
164261< 0.1%
 
158131< 0.1%
 
141851< 0.1%
 
138001< 0.1%
 

Duration
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct501
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.0493489
Minimum0
Maximum187650
Zeros469
Zeros (%)16.1%
Memory size22.8 KiB
2020-10-20T17:52:13.713880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median29
Q397
95-th percentile540.6
Maximum187650
Range187650
Interquartile range (IQR)93

Descriptive statistics

Standard deviation3864.178023
Coefficient of variation (CV)15.64132041
Kurtosis1948.525276
Mean247.0493489
Median Absolute Deviation (MAD)29
Skewness41.99159887
Sum720890
Variance14931871.79
MonotocityNot monotonic
2020-10-20T17:52:13.856596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
046916.1%
 
1963.3%
 
4752.6%
 
2742.5%
 
3642.2%
 
5481.6%
 
9441.5%
 
7441.5%
 
6371.3%
 
14351.2%
 
Other values (491)193266.2%
 
ValueCountFrequency (%) 
046916.1%
 
1963.3%
 
2742.5%
 
3642.2%
 
4752.6%
 
ValueCountFrequency (%) 
1876501< 0.1%
 
753301< 0.1%
 
297601< 0.1%
 
251071< 0.1%
 
214101< 0.1%
 

Month
Categorical

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
July
534 
September
325 
April
252 
May
252 
October
250 
Other values (7)
1305 
ValueCountFrequency (%) 
July53418.3%
 
September32511.1%
 
April2528.6%
 
May2528.6%
 
October2508.6%
 
August2468.4%
 
December2277.8%
 
June1926.6%
 
March1926.6%
 
November1836.3%
 
Other values (2)2659.1%
 
2020-10-20T17:52:13.994185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T17:52:14.107311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length6
Mean length5.934544208
Min length3

Interactions

2020-10-20T17:51:58.232139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:58.438827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:58.557207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:58.667256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:58.776560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:58.891442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:59.009828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:59.124528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:59.248467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:59.359777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:59.486401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:59.595432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:59.694541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:59.803079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:51:59.906077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:00.006933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:00.109504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:00.209167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:00.320001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:00.421310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:00.536363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:00.652408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:00.766609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:00.887348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:00.999017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:01.107119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:01.219422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:01.336005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:01.462093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:01.692012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:01.827073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:01.953200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:02.073091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:02.195284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:02.319551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:02.443278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:02.563168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:02.684536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:02.797269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:02.898766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:03.011241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:03.120622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:03.246628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:03.364795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:03.479719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:03.599009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:03.701904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:03.807413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:03.918329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:04.017886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:04.132516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:04.242108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:04.351663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:04.461126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:04.570359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:04.677938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:04.790562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:04.893218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:05.117460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:05.230446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:05.348654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:05.455346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:05.567437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:05.680463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:05.792214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:05.896390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:06.001822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:06.108660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:06.224017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:06.335111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:06.448954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:06.571812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:06.681235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:06.799716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:06.917876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:07.038309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:07.156570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:07.279466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:07.404710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:07.523226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:07.652898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:07.755302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:07.856971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:07.962422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:08.067334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:08.163106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:08.365529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:08.477737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:08.590202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:08.683740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:08.794448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:08.913089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:09.026906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:09.143459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:09.256267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:09.368620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:09.482579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:09.601408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:09.728757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:09.840664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-20T17:52:14.222766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-20T17:52:14.415295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-20T17:52:14.614980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-20T17:52:14.825531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-20T17:52:15.005285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-20T17:52:10.065861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T17:52:10.348173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

TempExMaxSpatExMaxTempDistSpatDistCoverageTimeLossCarTimeLossHGVStrasseAnzGesperrtFsEinzugRichtungLengthDurationMonth
0691518300181122903A 9121018January
1691518300181122903A 91111272947January
2691518300181122903A 90516361January
333530100171032909A 9121410January
433530100171032909A 9051260345January
533530100171032909A 90512588January
61741543200881060541A 71111322143January
757221000381202839A 701213246407January
82491022502581001978633A 711111005199January
960205600681110622A 70120062January

Last rows

TempExMaxSpatExMaxTempDistSpatDistCoverageTimeLossCarTimeLossHGVStrasseAnzGesperrtFsEinzugRichtungLengthDurationMonth
29083601406300841315512A 312103December
290975655400451995882A 3111122881December
29107565540702451995882A 305163920December
291181269800721048823A 711158560December
291281269800721048823A 70518511December
2913601221900261090859A 905111611December
2914601221900261090859A 91213054December
2915601221900261090859A 90511291December
29166095180601837756A 960505225December
291739221800491128688A 73121151839December

Duplicate rows

Most frequent

TempExMaxSpatExMaxTempDistSpatDistCoverageTimeLossCarTimeLossHGVStrasseAnzGesperrtFsEinzugRichtungLengthDurationMonthcount
46189192600841105630A 9311100September47
31361014092781127913A 312100November29
4517784590576241189798A 911100September20
5845622260576451778691A 911100September19
38111190300611937680A 9312100August8
56300290800861673678A 96111018October8
12152831140831467739A 911100July7
415283160831467739A 911100July6
10152831120831467739A 911100July6
18152831200831467739A 911100July6